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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞峰(Ruey-Feng Chang) | |
| dc.contributor.author | Yao-Sian Huang | en |
| dc.contributor.author | 黃耀賢 | zh_TW |
| dc.date.accessioned | 2021-06-16T02:45:33Z | - |
| dc.date.available | 2020-07-22 | |
| dc.date.copyright | 2015-07-22 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-07-19 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54225 | - |
| dc.description.abstract | 乳癌已經成為女性高致死率的疾病。除了治療之外，未來癌症的復發機率以及存活率也是重要的資訊。隨著醫療技術的進步，與乳癌相關之生物標記已定義並且進行相關研究。不同的生物標記組合，其治療的方式、復發機率以及存活年限不盡相同，尤其是三陰性腫瘤的預後以及復發是最差以及最快的。因此，及早取得生物標記資訊，有助於治療的方式及療後評估。超音波乳房攝影是一種便宜、非侵入式且不受乳房緻密度影響的影像，近年來已廣泛使用於乳房腫瘤檢查，隨著技術的進步，超音波影像搭配都卜勒效應原理，將腫瘤周圍之血管資訊顯示於影像上，提供更多資訊作為診斷依據。因此，為了建立有效率的的診斷程序，許多電腦輔助診斷系統以超音波影像為基礎被開發出來，將影像特徵量化提供醫師方便以及立即協助。在這篇論文中，我們藉由電腦輔助診斷系統量化超音波影像特徵，包含腫瘤以及血管特性，再根據量化後特徵資訊，評估不同生物標記種類腫瘤之間血管以及腫瘤形狀、紋路的關係。 | zh_TW |
| dc.description.abstract | Breast cancer has become the most fatal disease among women. Beside the treatment of breast cancer, the relapse and survival rate are also the important information. With the advance of medical technology, the biomarkers of breast cancer have been defined and investigated. Different subtypes of biomarkers response to the different treatment therapy, relapse, and survival rate, especially the triple-negative tumors that have the worst prognosis and highest relapse rate. Hence, it is helpful to determine the treatment planning and evaluate the prognosis if the biomarker information is obtained as early as possible. Ultrasound (US) is a cheaper, non-invasive, effective against to eliminate the influence of dense tissue, and has been widely used in breast examination. With the growth of US technique, the angiogenesis around tumor has been displayed on the US imaging with the Doppler effect and provided additional information for diagnosis. Therefore, for constructing the efficient diagnosing processes, various computer-aided diagnosis (CAD) systems have been developed based on US to quantify the imaging characteristics for supplying a convenient and immediate assistance. In this study, the morphology and texture features of angiogenesis and tumor are quantified by using the proposed CAD systems and then the correlations among distant subtypes of biomarkers are analyzed and evaluated. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T02:45:33Z (GMT). No. of bitstreams: 1 ntu-104-D96922017-1.pdf: 6036007 bytes, checksum: 9fd5c3b0672d8635716e9b551cf15036 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | ACKNOWLEDGEMENTS ii
摘 要 iii 乳房超音波之生物標記分析 iii ABSTRACT iv List of Figures viii List of Tables xi Chapter 1 Introduction 1 1.1. Research motivation 1 1.2. Issue Descriptions 6 1.2.1. Angiogenesis Identification 6 1.2.2. Imaging Characterization of TNBC and Fibroadenoma 8 Chapter 2 Review of Related Works 10 2.1. Introduction 10 2.2. Data Acquisition for 3-D Ultrasound 11 2.3. 3-D Ultrasound Imaging 12 2.3.1. Doppler Ultrasound 12 Chapter 3 Correlation between Angiogenesis and Biomarker 15 3.1. Introduction 15 3.2. Material 20 3.2.1. Patient Information and Demographics 20 3.2.2. Histopathologic analysis 21 3.2.3. 3-D Power Doppler US Imaging 22 3.3. Method 23 3.3.1. Preprocessing 23 3.3.2. 3-D Vascular Thinning 25 3.3.3. Tree Construction 29 3.3.4. Feature Extraction 31 3.3.4.1 Volume 32 3.3.4.2 Complexity 33 3.3.4.3 Length 34 3.3.4.4 Diameter 35 3.3.4.5 Tortuosity 36 3.4. Results 38 3.4.1. Statistical Analysis 38 3.4.2. Tumor Characteristics and Vascular Features of 3-D Power Doppler US 38 3.5. Discussion 50 3.6. Conclusion 55 Chapter 4 Classification between TNBC and Fibroadenoma 56 4.1. Introduction 56 4.2. Materials 58 4.2.1. Patient Information and Data Acquisition 58 4.3. Method 59 4.3.1. Tumor Segmentation 60 4.3.1.1 Contrast-enhanced gradient image 61 4.3.1.2 Level set method 62 4.3.1.3 Hole Filling 63 4.3.2. Ranklet Transform 65 4.4. Feature Extraction 67 4.4.1. Morphology features 68 4.4.2. Conventional texture features 69 4.4.3. Multi-resolution gray-scale invariant texture features 71 4.5. Support Vector Machine (SVM) 73 4.6. Classification 74 4.7. Statistical Analysis 75 4.8. Results 76 4.9. Discussion 85 4.10. Conclusion 89 Chapter 5 Conclusion and Future Directions 90 5.1. Conclusion 90 5.2. Future Directions 92 Reference 93 | |
| dc.language.iso | en | |
| dc.title | 乳房超音波之生物標記分析 | zh_TW |
| dc.title | Analyzing Biomarker in Breast Ultrasound | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 黃俊升(Chiun-Sheng Huang),張允中(Yeun-Chung Chang),陳啟禎(Chii-Jen Chen),羅崇銘(Chung-Ming Lo) | |
| dc.subject.keyword | 乳癌,超音波,電腦輔助診斷,都卜勒效應,三陰性腫瘤, | zh_TW |
| dc.subject.keyword | Breast cancer,ultrasound,computer-aided diagnosis,Doppler effect,triple-negative breast cancer, | en |
| dc.relation.page | 115 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-07-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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